COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopting a combination of Singular Value Decomposition (SVD), and Discrete Wavelet Transform (DWT). The combination of these two signal processing techniques is gaining lots of interest in the field of speaker and speech recognition. As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. The hybrid structure of DWT and SVD, adopted in this approach adds to its efficiency, where a 200 times reduction, in terms of the number of operations, is achieved. Despite the fact that symptoms of the infected and non-infected people used in the study are having lots of similarities, diagnosis results obtained from the application of the proposed approach show high diagnosis rate, which is proved through the matching with relevant PCR tests. The proposed approach is open for more improvements with its performance further assured by enlarging the dataset, while including healthy people.

Knowledge about, acceptance of and willingness to use over-the-counter COVID-19 self-testing kits

Objectives: Early investments in new diagnostic technologies that allow for rapid and decentralized testing were critical in reducing SARS-CoV-2's detrimental health and economic effects. This study evaluates public knowledge about, acceptance of and willingness to use COVID-19 self-testing kits. Methods: An online descriptive cross-sectional questionnaire was used in this study. The final study population included all contacted national and resident adults, age 18 and over, who were willing to engage in the study. The survey was divided according to participants' demographic information and 11 questions assessed the respondents' understanding of and willingness to use COVID-19 self-testing kits. The statistical analysis was carried out using SPSS version 24. Multivariate linear regression models were used to identify the factors influencing respondents' knowledge of and attitudes toward the acceptability of self-testing kits for COVID-19 and their willingness to use these kits. Key findings: A total of 876 respondents participated in the study and completed the whole questionnaire. The average knowledge score on the acceptability of and willingness to use self-testing kits for COVID-19 was 70.2%, with a 95% confidence interval (CI) [69.1%, 71.4%]. Participants who were postgraduate, female and vaccinated against COVID-19, as well as employees and older participants, were jointly highly associated with higher levels of knowledge about, acceptance of and willingness to use self-testing kits for COVID-19. Moreover, participants who had been infected with COVID-19, were vaccinated against COVID-19 or were female, employees, older, Western or Arabic were jointly highly associated with positive attitudes about the acceptability of and willingness to use self-testing kits for COVID-19. Conclusions: The majority of the respondents have acceptable levels of knowledge about, acceptance of and willingness to use self-testing kits for COVID-19. Nonetheless, future studies should consider the issues of pre- and post-test counselling, false negative results and the sale of unregulated testing kits. Additional information should be communicated so that people can make informed decisions and be protected from possible abuse of COVID-19 self-testing kits when they become available in pharmacies.

No Significant Increase of Guillain-Barré Syndrome after COVID-19 Vaccination in Adults: A Vaccine Adverse Event Reporting System (VAERS) Study

Objective: To investigate whether there is an association between Guillain-Barré syndrome (GBS) and COVID-19 vaccination. Background: On July 12, 2021, the US Food and Drug Administration (FDA) released a new warning that Johnson & Johnson COVID-19 vaccine could increase the risk of developing GBS. Design/Methods: The reporting rate of adult GBS after COVID-19 vaccination, ascertained with Brighton criteria, was compared to the rate after Influenza and all other vaccinations: preCOVID (January 2019-August 2019), pre-vaccine (April 2020-November 2020);and the vaccine period (December 2020-July 2021). Results: 513, 2, and 13 patients reported GBS after COVID-19, Influenza and all other vaccinations during the COVID-19 vaccination time period, respectively. The reporting rate of GBS after COVID-19 vaccination was significantly higher than after Influenza and other vaccinations (26.5, 0.15, 1.21 per 10 million, p<0.0001). However, the rate is within the incidence range of GBS in the general population. Using self-controlled and case centered analysis, there is a significant difference in the reporting rate of GBS after COVID-19 vaccination between the risk period and control period (p<0.0001). The reporting rate of GBS after the Johnson and Johnson vaccine was significantly higher than after Moderna and Pfizer (p<0.0001). Similar results were obtained when all patients, regardless of Brighton criteria, were included. Conclusions: Although the incidence of GBS after COVID-19 vaccination was not statistically different than that of the general population, the increased reporting of GBS within the first 12 weeks after COVID-19 vaccination (more so than with other vaccinations) suggests that some cases of GBS are temporally associated with COVID-19 vaccination. However, there is a reduction in the reporting rate of GBS after other vaccines (compared to reporting rates preCOVID), highlighting limitations inherent in any passive surveillance system. These findings warrant continuous analysis of GBS after COVID-19 vaccination. Further controlled studies are needed to investigate the association of GBS after COVID-19 vaccination.

Modular Construction of COVID-19 Hospital

The impact of the COVID-19 pandemic is well known to everyone around the globe. This has increased the need for hospital beds to accommodate a large number of COVID patients. Thus, the main objective of this paper is the rapid building of a hospital considering sustainability and risks in the construction. The authors have presented detailed planning which includes project life cycle, work breakdown structure (WBS), and cost analysis of a COVID-19 care hospital to be constructed in 45 days. The hospital is a single-storied building planned to accommodate 200 patients. The roof structure and external walls are made of lightweight sandwich panels. Columns, beams, and truss elements present in the interior are made of lightweight and high-strength steel. The estimation and PERT analysis to construct the project is included. While comparing the cost of all the activities during the construction phase, it is found that most of the expenditure is on the construction of grade slabs. The maximum time is allocated for MEP work as it needs a lot of coordination between civil and mechanical engineers. A network diagram is presented for understanding the sequence of all the activities to be followed. © IEOM Society International.

Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

The novel coronavirus (COVID-19) has become widespread around the world. It started in Wuhan, China, and has since spread rapidly among people living in other countries. Hence, the World Health Organization has considered COVID-19 as a pandemic that threatens millions of people's lives. Due to the high number of infected people, many hospitals have been facing critical issues in providing the required medical services. For instance, some clinical centers have been unable to provide one of the most important medical services, namely blood tests to determine whether an individual is infected with COVID-19. Therefore, it is important to present an alternative diagnosis option to prevent the further spread of COVID-19. In this paper, a proposed intelligent detection communication system (IDCS) is configured for distributed mobile clinical centers to control the pandemic. In addition, the intelligent system is integrated with the Zigbee communication protocol to build a mobile COVID-19 detection system. The proposed system was trained on X-ray COVID-19 lung images used to identify infected people. The Zigbee protocol and decision tree algorithm were used to design the IDCS. The results of the proposed system show high accuracy 94.69% and accept results according to the performance measurements. © 2021. All Rights Reserved.